If we think of the parser as dynamically converting G into a CFG G , then this CFG is likewise able to compositionally reconstruct TSG, TIG, or RF-TAG derivations-we say that G simulates G. Note that the parser specifies how to convert G into G , but G is not itself a parser.

these three formalisms have a special relationship to CFG that is independent of any particular parsing algorithm: for any TSG, TIG, or RF-TAG G, there is a CFG that simulates G. We make this notion more precise below.


(In this variant adjunction at foot nodes is permitted.)

the only kinds of adjunction which can occur to unbounded depth are off-spine adjunction and adjunction at foot nodes.


The only functions on a back-pointer the parser can compute online are the identity function (by copying the back-pointer) and constant functions (by replacing the back-pointer); any other function would have to dereference the back-pointer and destroy the structure of the algorithm.

such functions must be computed offline.


In fact, for technical reasons we will use this notion instead of strong equivalence for testing the strong generative power of a formal system.

the original problem, which was, given a formalism F , to find a formalism that has as much strong generative power as possible but remains weakly equivalent to F , is now recast as Figure 4: CFG which simulates the grammar of Figure 3.


for all n-predicates P n , with respect to a model M , an SMRS can be viewed as denoting an element of P(G), where G is the set of variable assignment functions (i.e., elements of G assign the variables e, .


As it follows from Definition 3, each saturation of a terminal DV-structure $ has the same set of nodes and a strictly narrower set of valencies.

any terminal DV-structure has maximal saturations with respect to the order relations is a D-tree.


While we conceed that alternative techniques that are not sound (Black 1989, (Johnson 1998, Rayner and Carter 1996 may still be useful for many purposes, we prefer sound approximations because there is no chance that the correct hypothesis will be eliminated.

further processing techniques (for instance, N-best search) will still have an opportunity to find the optimal solution.


The boolean feature [∪±] of each node controls whether it must be flattened out or not.

infinitives in canonical position are assigned [∪+]: the above licenses the following tree of word order domains: As a consequence, Reape's theory correctly predicts scrambling (2,3) and full extraposition (4), but cannot handle the partial extraposition in (5): (2) (dass) Maria einen Mann zu lieben versucht (3) (dass) einen Mann Maria zu lieben versucht (4) (dass) Maria versucht, einen Mann zu lieben (5) (dass) Maria einen Mann versucht, zu lieben Our approach is based on dependency grammar.


One requires it to land in canonical position: field ext (zu lieben 1 ) = {vc} the other requires it to be extraposed: In the canonical case, zu lieben 1 does not offer field mf and einen Mann must climb to the finite verb: In the extraposed case, zu lieben 2 itself offers field mf: The ID tree for (9) is: The lexical entry for the bare infinitive lieben requires it to land in a vc field: field ext (lieben) = {vc} therefore only the following LP tree is licensed: 3 (dass) Maria einen Mann lieben wird where mf ≺ vc ≺ v, and subject and object, both in field mf, remain mutually unordered.

we correctly license (9) and reject (10).


(dass) Maria wird einen Mann lieben können xf (14) is ruled out because können must be in the vc of wird, therefore lieben must be in the vc of können, and einen Mann must be in the mf of wird.

einen Mann must precede both lieben and können.


This method leads to a very superficial judgment of AMs for the following reasons: (1) The identification results are based on small subsets of the candidates extracted from the corpus.

results achieved by individual measures may very well be due to chance (cf.


9 The -axis represents the percentage of data processed in the respective SL, while the   ) contain a particularly small number of TPs.

they are more susceptible to random variation, which illustrates that evaluation based on a small number of ¦ -best candidate pairs cannot be reliable.


279 880 for PNV), manual inspection of the entire data is impractical.

we use random samples from the candidate sets to obtain estimates for the proportion of true collocations among the low-frequency data.


In order to obtain reliable estimates, we have used an exact test based on the following model: Assuming a proportion of TPs in the full low-frequency data (AdjN or PNV), the number of TPs in a random sample of size¨is described by a binomially distributed random variable ¡ with parameter .

11 the probability of finding ¢ or less TPs in the sample is .


Since the extraction module (see Section 2.3) analyzes the usage of the input term in retrieved pages, pages not containing the term are of no use for our purpose.

we use as the retrieval module "Google," which is one of the major search engines and does not conduct query expansion 1 .


Then we discard such c's whose P (d|c) is below a specific threshold.

for the input term, related domains and descriptions are simultaneously selected.


As a result, for the input term, related domains and descriptions are simultaneously selected.

we do not have to know a priori which domains are related to each term.


However, since Google (i.e., the search engine used in our system) rates the quality of pages based on hyperlink information, and selectively retrieves those with higher quality (Brin and Page, 1998), we tentatively regarded P Q (d) as a constant.

in practice the description model is approximated solely with the language model as in Equation 4.


First, while general language models quantify the extent to which a given word sequence is linguistically acceptable, our model also quantifies the extent to which the input is acceptable as a term description.

we trained the model based on an existing machine readable encyclopedia.


We used the ChaSen morphological analyzer to segment the Japanese CD-ROM World Encyclopedia (Heibonsha, 1998) into words (we replaced headwords with a common symbol), and then used the CMU-Cambridge toolkit (Clarkson and Rosenfeld, 1997) to model a word-based trigram.

descriptions in which word sequences are more similar to those in the World Encyclopedia are assigned greater probability scores through our language model.


We selected the top three (not one) descriptions for each domain, because reading a couple of descriptions, which are short paragraphs, is not laborious for human users in real-world usage.

at least one description was generated for 85 test terms, disregarding the correctness.


This is particularly a problem for the Verbmobil task, where the word order of the German-English sentence pair can be quite different.

the word order of the automatically generated target sentence can be different from that of the target sentence, but nevertheless acceptable so that the WER measure alone can be misleading.


Given a logical formula , a model genera-tor is a program which builds some of the models satisfying this formula.

for parsing, a model generator can be used to enumerate the (minimal) model(s), that is, the parse trees, satisfying the conjunction of the lexical categories selected on the basis of the input string plus any additional constraints which might be encoded in the grammar.


Lexical lookup only returns these categories in the grammar whose semantics subsumes some portion of the input semantics.

if some grammar rule involves a daughter category whose semantics is not part of the mother semantics i.e.


• A non-finite verb depending on V2 can go into the right bracket.

2 it opens a reduced phrase with only one position for a verbal dependent (see Section 2.8 for another possibility).


Finally, the existence of this ambiguity is also confirmed by the contrast between full infinitives (with zu) and bare infinitives (without zu): Bare infinitives cannot form an embedded domain outside of the Vorfeld.

there are two different prosodies for (3a) (with or without detachment of das Buch 'the book' from zu lesen 'to read'), whereas only one prosody without detachment is permitted for (3b), although (3a) and (3b) have isomorphic dependency trees.


If that too fails, then, she herself generates a clarification.

the prediction made by this protocol is that A will tend to initially interpret (12(2)) as a response to her question, not as a clarification: 12A(1): Who do you think is the only person that admires Mary?


This formalism, which derives from current type-logical grammars, is abstract in the sense that both syntax and semantics are handled by the same set of primitives.

the formalism is reversible and provides different computational paradigms that may be freely composed together.


In these frameworks lexical entries carry a significant amount of information including subcategorisation information.

the practical parse success of a grammar is significantly dependent on the quality of the lexicon.


The ANLT morphological analyser performs regular inflectional morphology using a unification grammar for combining morphemes and rules governing spelling changes when morphemes are concatenated.

a plural noun such as patients is composed of the morphemes patient and +s with the features on the top node being inherited partially from the noun and partially from the inflectional affix: In dealing with word tag pairs, we have used the word grammar to treat the tag as a novel kind of affix which constrains the category of the lexical entry it attaches to.


For the purposes of parsing, these larger units can be treated as words, so the grammar does not need to contain special rules for 'multi-word' words: The same technique can be used to package up a wide variety of formulaic expressions which would cause severe problems to most handcrafted grammars.

all of the following 'words' have been identified using fsgmatch rules and can be passed to the parser as unanalysable chunks.


the rules are (manually) assigned reliabilities which divide the rules into reliability classes, with the most reliable ("bullet-proof") group of rules applied first and less reliable groups of rules (threatening to decrease the 100% recall) being applied in subsequent steps.


However, in the majority of phenomena the correct disambiguation requires a much wider context.

the rules use as wide context as possible with no context limitations being imposed in advance.


If conceived in this way, an annotated corpus can be used as a resource for linguistic research not only within the limits of the sentence, but also with regard to discourse patterns.

the applications of the research to issues of information retrieval and extraction may be made more effective; also applications in new domains become feasible, be it to serve for inner linguistic (and literary) aims, such as text segmentation, specification of topics of parts of a discourse, or for other disciplines.


An example is: We find an entailment between producing, or making and selling goods, derived from Word-Net, since synset ' make, produce, create( has the genus manufacture, defined in the gloss of its homomorphic nominalization as "for sale".

the semantic form of question Q261 and its illustrated answer are similar.


This shows that the main power of the maxent model comes from a a very small subset of the possible features.

if memory and speed are concerned, the incremental feature selection is highly recommended.


5 Note that RDF descriptions function much like class definitions in an object-oriented programming language: they provide, effectively, templates that describe how objects may be instantiated, but do not constitute the objects themselves.

in a document containing an actual annotation, several objects with the type argument may be instantiated, each with a different value.


The third best system (F=77.37%) was Borthwick's ME system enhanced with hand-crafted rules and dictionaries (1999).

the best three systems used quite different approaches.


Thus, the last word of an NE is often a head that is more useful than other words for the classification.

we register the last word into a suffix dictionary for each non-numerical NE class (i.e., ORGANIZA-TION, PERSON, LOCATION, and ARTIFACT) in order to accept only reliable candidates.


For instance, we get the following rule when a person's name is incorrectly tagged as a location's name by a POS tagger.

*:all-kanji:location-name -> PERSON,0,0 we have to consider a way to refine the recognition rules.


By comparing the candidates with the given answer for the training data, we can classify them into positive examples and negative examples for the recognition rule.

we can apply decision tree learning to classify these examples correctly.


1) in order to distinguish them from recognition rules.

each recognition rule is enhanced by a set of dt-rules.


Otherwise, we selected the majority class in the list.


Short NEs like 'Washington' are often ambiguous, but longer NEs like 'Washington State University' are less ambiguous.

short recognition rules often have dozens of dt-rules, whereas long rules have simple constraints.


This test showed that the difference was not significant (p ≈ 0.1).

the MCLE PCFG did not perform significantly better than the MLE PCFG in terms of precision and recall.


Yet adding this term improves tagging accuracy considerably, to 95.3%.

for bitag tagging at least, the conditional model has a considerably higher error rate than any of the joint models examined here.


Contrary to the French Treebank, the Penn Treebank contains non−surfastic constructions such as empty nodes, and constituents that are not triggered by a lexical items.

before evaluating our new shallow− parser, we automatically removed from the test sentences all opening brackets that were not immediately followed by a lexical item, with their corresponding closing brackets, as well as all the constituents which contained an empty element.


The number of (possibly zero-size) spans for a sentence of length ¥ is fixed: .

to be able to evaluate and model the total edge counts, we look to the number of edges over a given span.


12 Assume that the phrasal categories in n can match any span (or any non-zero span in NOEMPTIES).

13 phrasal categories in n do not constrain whether c can match a .


For example, in TRIE-NOEMPTIES the low-span saturation is lower than in TRIE-NOTRANSFORM since short spans in the former case can match only signatures which have both d and o small, while in the latter only d needs to be small.

the several hundred states which are reachable only via categories all match every span starting from size 0 for NOTRANSFORM, but are accessed only gradually for NOEMPTIES.


Our approach follows a strategy of positing and attaching nodes only if sufficient evidence can be found in the instance base.

the latter two categories cannot really be considered errors in the strict sense.


In a third set of experiments, we merged corrections with normal turns to form a 2-way distinction over all between aware turns and all others.

we only distinguish turns in which a user first becomes aware of an ASR failure (our original isaware and corraware categories) from those that are not (our original corr and norm categories).


These methods typically treat each word feature as independent, and seldom consider linguistic factors such as the context or lexical chain relations among the features.

these methods are not good at discriminating a large number of documents that typically lie near the boundary of two or more topics.


Here we use the semantic correlations defined in Section 3 to derive the semantic relationship between t j and any other term t k .

: For each pair (t j ,t k ), k=1,..n, set For all t k with Link(t j ,t k )=1, we form a semantic group centered around t j denoted by: Here t j is the main keyword of node G j and is denoted by main(G j )=t j .


From the result of Equation (13), we have: If Y (i) > 0; then topic T i is present; otherwise T i is not present in document x The use of Equation (13) implies that: a) If a LPN is not activated, i.e., y (i) ≤ θ (i) , then its output is not used in the GE.

it will not affect the output of other LPN.


Finally, the entire sentence is extracted, including any subordinate clauses, although we are working on refinements involving sentence compaction.

we believe that more work is required before the sentential descriptions can be fully integrated into the biographies.


The comparison was automatic, measuring similarity among vectors of content words (i.e., stop words such as articles and prepositions were removed).

here is an example to further clarify the strict standard of the automatic evaluation (words scored correct are underlined): System: E. Lawrence Barcella is a Washington lawyer, Washington white-collar defense lawyer, former federal prosecutor System Merge: Washington white-collar defense lawyer human Merge: a Washington lawyer and former federal prosecutor Automatic Score: Correct=2; Extra-Words=2; Missed-Words=3 although 'lawyer' and 'prosecutor' are synonymous in WordNet, the automatic scorer doesn't know that, and so 'prosecutor' is penalized as an extra word.


Since both of these 'referent set composition' operations at the conjunctive nodes -as well as the union operation at the disjunctive nodes -are linear in space and time on the number of elements in each of the composed sets (assuming the sets are sorted in advance and remain so), the calculation of referent sets only adds a factor of ¤ § ¤ to the size complexity of the forest and the time complexity of processing it, where ¤ § ¤ is the number of objects and events in the run-time environment.

the total space and time complexity of the above algorithm (on a context-free forest) is .


The text segmentation algorithm described in this paper is intended to be applied to the summarization of documents or speeches.

it should be able to handle domain-independent texts.


An algorithm that can provide a solution for Step 2 will be a simpler version of the algorithm used to find the maximumprobability solution in Japanese morphological analysis (Nagata, 1994).

a solution can be obtained by applying a dynamic programming (DP) algorithm.


For example, the algorithm divides a newspaper editorial consisting of about 27 sentences into 4 to 6 segments, while on the other hand, it divides a long text consisting of over 1000 sentences into 10 to 20 segments.

the number of segments is not proportional to text length.


Another major difference from their algorithm is that our algorithm does not require training data to estimate probabilities, while their algorithm does.

our algorithm can be applied to domain-independent texts, while their algorithm is restricted to domains for which training data are available.


Their approach is indirect compared with our approach, which directly finds the maximumprobability segmentation.

their model can not straightforwardly incorporate features pertaining to a segment itself, such as the average length of segments.


Obviously, stealing eliminates the exclusive ownership of the agenda and unchecked foreign edge lists of the respective threads.

a thread needs to lock its agenda and edge lists each time it needs to access it.


Several research efforts have explored the use of dialogue act tagging schemes for tasks such as improving recognition performance (Reithinger and Maier, 1995;Shriberg et al., 2000), identifying important parts of a dialogue (Finke et al., 1998), and as a constraint on nominal expression generation (Jordan, 2000).

we decided to explore the application of a dialogue act tagging scheme to the task of evaluating and comparing dialogue systems.


However, examination of the dialogue transcripts suggested that the ANY category sometimes arose as a rational reaction by the caller to repeated recognition error.

we decided to distinguish the cases where the user completed the assigned task, versus completing some other task, versus the cases where they hung up the phone without completing any itinerary.


This would allow us, for example, to investigate what part of the task an utterance contributes to separately from what speech act function it serves.

a central aspect of DATE is that it makes distinctions within three orthogonal dimensions of utterance classification: (1) a SPEECH-ACT dimension; (2) a TASK-SUBTASK dimension; and (3) a CONVERSATIONAL-DOMAIN dimension.


We believed it plausible that this was due to the fact that flight presentation utterances are longer than other system turns.

this metric simply captures whether or not the system got enough information to present some potential flight itineraries to the user.


Number of clustering targets in the preceding class increases to Î ¾ from Î in 2grams, and the length of the vector in the succeeding class also increase to Î ¾ .

efficient word clustering is needed to keep the reliability of 3-grams after the clustering and a reasonable calculation cost.


Replace the frequent word pair in training data to word succession, and recalculate the frequency of the word or word succession pair.

the summation of probability is always kept to 1.


The number of combinations for the first two words of the word successions is at most the number of word successions.

the number of increased parameters in the Multi-Class Composite 3-gram is at most the number of introduced word successions times 2.


This word clustering is performed based on the word connectivity in the corpus.

the Multi-Class N-grams based on Multi-Class can improve reliability with a compact model size without losing accuracy.


In the Multi-Class Composite N-grams, higher order word N-grams are introduced through the grouping of frequent word successions.

these have accuracy in higher order word N-grams added to reliability in the Multi-Class N-grams.


And the number of increased parameters with the introduction of word successions is at most the number of word successions times 2.

multi-Class Composite 3-grams can maintain a compact model size in the multi-Class N-grams.


An advantage of using this dataset is that a large number of state of the art statistical natural language processing methods have already been applied to the data.

we can readily compare our results with other reported results.


However, this is rather ad hoc since it is unclear what is the best way to do so.

in practice, it can be quite difficult to implement this idea properly.


However, using more features requires more memory and slows down the algorithm.

in practice it is still necessary to limit the number of features used.


In many maxent applications, the f i take on only the values 0 or 1, and thus, typically, max j,y f i (x j , y) = 1.

instead of slowing by a factor of f # , there may be no slowing at all!


The space of models in the form of Equation 1 is convex, with a single global optimum.

gIS and SCgIS are guaranteed to converge towards the same point.


GIS requires the training matrix, of size T , the λs, of size F , as well as the expected and observed arrays, which are also size F .


The bottom loops also require time O(T ).

one iteration of SCGIS takes about as long as one iteration of GIS, and in practice in our im-plementation, each SCGIS iteration takes about 1.3 times as long as each GIS iteration.


The speedup in SCGIS comes from the step size: the update in GIS is slowed by f # , while the update in SCGIS is not.

we expect SCGIS to converge by up to a factor of f # faster.


In Section 4, we in fact empirically observe that fewer iterations are required to achieve a given level of convergence, and this reduction is very roughly proportional to f #.

the speedup does appear to be because of the larger step size.


For such applications, the array s [j, y] would be of a size at least 5000 times the number of training instances: clearly impractical (but see below for a recently discovered trick).

it is unsurprising that this speedup was forgotten.


However, for training instances where f # (x j , y) < f # , the IIS update can take a proportionately larger step.

iiS can lead to speedups when f # (x j , y) is substantially less than f # .


Thus, instead of training one model with a time proportional to 10,000, we train two models, each with time proportional to 100.

in this example, there is a 50 times speedup.


The objective function measure is less noisy than test data entropy, and test data entropy is less noisy than test data error rate: the noisier the data, the more chance of an unexpected result.

one possibility is that these cases are simply due to noise.


It requires very few additional resources: unless there are a large number of output classes, it uses about as much space as standard GIS, and when there are a large number of output classes, it can be combined with our clustering speedup technique (Goodman, 2001) to get both additional speedups, and to reduce the space requirements.

there appear to be no real impediments to its use, and it leads to large, broadly applicable gains.


Select an example for a given question type.

for BIRTHYEAR questions we select "Mozart 1756" (we refer to "Mozart" as the question term and "1756" as the answer term).


If x contains fewer a's than b's, then PRODUCE And if a's and b's appear equally often in x, then PRODUCE(x) is the union of the two sets.

while the x-to-z mapping is not a regular relation under this grammar, at least PRODUCE(x) is a regular set for each x-just as finite-state OT constraints, notably Koskenniemi's (1983) two-level model, which like OT used finite-state constraints on candidates y that encoded an alignment between underlying x and surface z. guarantees.


And if k can be arbitrarily large, then no finite-state automaton will handle all cases.

constraints like NOCODA do not allow an upper bound on k for all x ∈ Σ * .


We require harmony orderings to be compatible with GEN: an ordering must treatȳ ,ȳ as incomparable (neither is the other) if they were produced from different underlying forms.

9 To make the notation readable let us denote the relation by the letter H. a transducer for H accepts the pair (ȳ ,ȳ) ifȳ ȳ.


If Y i−1 is regular, then so is Y i since (as we will show) • C i and maps x to the set of starred candidates that C i will prune; ¬ denotes the complement of a regular language; and D is a transducer that removes all 's.

pRODUCE = Y n • pRON is regular as claimed.


Trivially, if a feature is introduced at a type in one module, then it is not appropriate to any type in any other module.

a frame for a TFS only needs to allow for the features appropriate to the 4 Prolog terms require one additional unbound variable per TFS (sub)term in order to preserve the intensionality of the logic -unlike Prolog terms, structurally identical TFS substructures are not identical unless explicitly structure-shared.


Whenever we bind a variable to a substructure of a TFS, filling pushes the TFS's encoding up within the same set to some other encoding.

at any given point in time during a computation, we do not exactly know which encoding we are using to represent a given TFS.


In UDRT, scope decisions are modelled as subordination constraints.

sentence (6) shows that subordination constraints may affect more than one pair of labels.


the source label with some target label # ¢ 334 5 .

we still need a proviso that only such labels can be linked that were determined to be parallel to the source label in the same sequence of ellipsis resolutions.


Generally, a quantifier that may or may not be raised out of the source is only copied if it gets scope inside the source.

the exact determination of the semantic material to be copied (i.e.


A single algorithm may be tuned to match a particular author's style, but parameterization will be necessary to match a variety of styles.

it is extremely difficult to exactly match any particular text without having the ability to adjust the pronominalization algorithm.


Recall that at each step in the loop going over the properties of various (disjunctive) lengths, the incremental algorithm adds to the description being built any property that is true of the target set and such that the current distractor set is not included in the set of objects having that property.

in the first loop over properties of length one, the algorithm will select the property , add it to the description and update the distractor set to .


Rather than a right-to-left search from each anaphoric NP for the first coreferent NP, we hypothesized that a right-to-left search for a highly likely antecedent might offer more precise, if not generally better coreference chains.

we modify the coreference clustering algorithm to select as the antecedent of NP£ the NP with the highest coreference likelihood value from among preceding NPs with coreference class values above 0.5.


When used in combination, the modifications consistently provide statistically significant gains in precision over the Baseline system without any loss in recall.

6 we observe reasonable increases in F-measure for both classifiers and both data sets.


The algorithm proposed by Corazza and Lavelli (1994) involves copying of charts, and this makes it very similar in behaviour to the former approach.

in both algorithms parts of the input grammar £ § are copied where a nonterminal occurs more than once, which destroys the compactness of the representation.


It is clear that the BULM derivation converts a parse tree into a unique sequence of parse events, and a valid event sequence corresponds to a unique parse tree.

a parse tree can be equivalently represented by a sequence of events.


In our experiments we used a uniform source language model over the words in the dictionary.

our spelling correction algorithm selects the word Ö that maximizes È Å ´Û Öµ.


An uncompressed LM is usually too large for practical use since all realistic applications have memory constraints.

lM pruning techniques are used to produce the smallest model while keeping the performance loss as small as possible.


This seems to be a very tedious task since for each model size, a large number of settings (θ 1 θ 2 ) have to be tried for finding the optimal ones.

we convert the problem to the following one: How to find an optimal function θ 2 =f(θ 1 ) by which the optimal threshold θ 2 is defined for each threshold θ 1 .


When we which to refer to a cluster of a word w in general we will use the notation W. The ACM estimates the probability of w i given the two preceeding words w i-2 and w i-1 as the product of the following two probabilities: (1) The probability of the predicted cluster PW i given the preceding conditional clusters CW i-2 and CW i-1 , P(PW i |CW i-2 CW i-1 ), and (2) The probability of the word given its cluster PW i and the preceding conditional clusters CW i-2 and CW i-1 , P(w i |CW i-2 CW i-1 PW i ).

the ACM can be parameterized by The ACM consists of two sub-models: (1) the cluster sub-model P(PW i |CW i-2 CW i-1 ), and (2) the word sub-model P(w i |CW i-2 CW i-1 PW i ).


We have 2 is independent of the clustering used.

for the selection of the best clusters, it is sufficient to try to maximize This is very convenient since it is exactly the opposite of what was done for conditional clustering.


In addition, we allow different values of the threshold for different sub-models.

we need to optimize the model parameter set l, j, k, t c , t w .


Therefore, we used two separate trees to store probabilities and backoff weights, respectively.

we used four tree structures to store ACMs in practice: two for the cluster sub-model P(PW i , and two for the word sub-model .


The results indicate that because of its better smoothing, when the backoff rate increases, the CER using the ACM does not increase as fast as that using the word trigram model.

we are reasonably confident that some portion of the benefit of the ACM comes from its better smoothing.


More complex caching techniques are possible (Goodman, 2001), but are not necessary for this experiment.

if lexical effects are present, we expect the model that uses caching to provide lower entropy estimates.


The extracted thesaurus does not distinguish between different head senses.

we convert the Roget's and Macquarie thesaurus into head term ordered format by combining each small sense set that the head term appears in.


Interpretation of noun compounds (NCs) is highly dependent on lexical information.

we explore the use of a large corpus (Medline) and a large lexical hierarchy (MeSH, Medical Subject Headings) to determine the relations that hold between the words in noun compounds.


A set of relations for knives, and example NCs exhibiting these relations is shown below: (Used-in): kitchen knife, hunting knife (Made-of): steel knife, plastic knife (Instrument-for): carving knife (Used-on): meat knife, putty knife (Used-by): chef's knife, butcher's knife Some relationships apply to only certain classes of nouns; the semantic structure of the head noun determines the range of possibilities.

if we can capture regularities about the behaviors of the constituent nouns, we should also be able to predict which relations will hold between them.


If our hypothesis holds (that NCs that fall within the same category pairs are assigned the same relation), then if most of the NCs fall within only a few category pairs then we only need to determine which relations hold between a subset of the possible pairs.

the more clumped the distribution, the easier (potentially) our task is.


The algorithm in figure 1(b) can be considered to build a series of hypotheses is the scoring function from the algorithm trained on just the first is .

the training algorithm can be considered to construct a sequence of models, .


A number of categories are annotated: the usual people, organization and location categories, as well as less frequent categories such as brand-names, scientific terms, event titles (such as concerts) and so on.

we created a training set of 53,609 sentences (1,047,491 words), and a test set of 14,717 sentences (291,898 words).


However, the LFG grammar treats it as the OBJ of the matrix clause.

the labeled bracketed version of this sentence does not receive a full parse, even though its unlabeled, string-only counterpart is wellformed.


Under the current definition of properties, we can introduce such a property by modifying the underlying unification grammar to produce a certain "diacritic" feature in a parse just in case the parse actually contains the appropriate reduced relative construction.

while properties are required to be local relative to features, we can use the ability of the underlying unification grammar to encode essentially arbitrary non-local information in features to introduce properties that also encode non-local information.


First, note that (2) implies that: Note that P({ω : f ∈ ω}|y i ) involves the sum of weights over all x ∈ X subject to the conditions that N (x) = 1 and α f (x) = 1.

p({ω : f ∈ ω}|y i ) can also be expressed in a form that is easy to evaluate using graphical techniques.


By the same arguments as above, d(V i ) only contains variables ordered after X i , so V n =x n .

we can evaluate the V i in the order V n , .


• We could use additional language models by using features of the following form: • We could use a feature that counts how many entries of a conventional lexicon co-occur in the given sentence pair.

the weight for the provided conventional dictionary can be learned.


So far, in machine translation research does not exist one generally accepted criterion for the evaluation of the experimental results.

we use a large variety of different criteria and show that the obtained results improve on most or all of these criteria.


The decoding parser is modified to allow inserting zero-fertility words, but unlimited insertion easily blows up the memory space.


First, the syntax of Chinese is not extremely different from English, compared with other languages such as Japanese or Arabic.

the TM could not take advantage of syntactic reordering operations.


Also, the pruning methods described in Section 5 use syntactic statistics from the training corpus.

we are now investigating more sophisticated LMs such as (Charniak, 2001) which  Figure 4: Effect of PCFG and re-training: No CFG probability (PCFG) was used (left).


To judge the quality of a machine translation, one measures its closeness to one or more reference human translations according to a numerical metric.

our MT evaluation system requires two ingredients: 1. a numerical "translation closeness" metric 2. a corpus of good quality human reference translations We fashion our closeness metric after the highly successful word error rate metric used by the speech recognition community, appropriately modified for multiple reference translations and allowing for legitimate differences in word choice and word order.


The first candidate recalls more words from the references, but is obviously a poorer translation than the second candidate.

naïve recall computed over the set of all reference words is not a good measure.


Candidate translations longer than their references are already penalized by the modified n-gram precision measure: there is no need to penalize them again.

we introduce a multiplicative brevity penalty factor.


It is important to note that the more reference translations per sentence there are, the higher the score is.

one must be cautious making even "rough" comparisons on evaluations with different numbers of reference translations: on a test corpus of about 500 sentences (40 general news stories), a human translator scored 0.3468 against four references and scored 0.2571 against two references.


(2) Ed wrote books Ò:Ed´×ÒÒµ Ò:λx λy write´y xµ Ò:books ×ÒÒ : λy write´y booksµ × : write´Ed booksµ Derivations like (2) give rise to the usual sort of predicate-argument structure whereby the order in which the arguments appear (and are bound by the λ's) is essentially constitutive of their meaning.

the first argument could be taken to correspond to the writer, whereas the second argument corresponds to what is being written.


Contextual reference can be modeled as a statement that from the current state (anaphor) there should be an accessible antecedent state at which particular conditions hold.

assuming an accessibility relation XS, we can model the meaning of the pronoun he as in (12).


In addition to using the syntactic feature sinf , we allow intonation marking to instantiate the values of the semantic informativity feature inf .

we have the following sort of entry: We therefore straightforwardly reap the syntactic benefits of Steedman's intonation analysis, while IS itself is determined via DGL's logical form rewriting system operating on the modal indications of informativity produced during the derivation.


Note that the category set we are using distinguishes around 400 distinct types; for example, tensed transitive buy is treated as a distinct category from infinitival transitive buy.

this evaluation criterion is much more stringent than that for a standard pos-tag label-set (there are around 50 pos-tags used in the Penn Treebank).


10 Of these, 10 (41.7%) were recovered correctly by the parser; 10 were incorrect because the wrong category was assigned to the relative pronoun, 3 were incorrect because the relative pronoun was attached to the wrong noun, and 1 was incorrect because the wrong category was assigned to the predicate from which the object was 9 Currently all the modifiers in nominal compounds are analysed in CCGbank as N N, as a default, since the structure of the compound is not present in the Penn Treebank.

the scores for N N are not particularly informative.


Since CCG produces unary and binary branching trees with a very fine-grained category set, CCG Parseval scores cannot be compared with scores of standard Treebank parsers.

we also evaluate performance using a dependency evaluation reported by Collins (1999), which counts wordword dependencies as determined by local trees and their labels.


if we split the data according to the ana np feature into personal and possessive pronouns (PPER PPOS), proper names (NE), and definite NPs (def NP).

we performed Co-Training experiments on subsets of our data defined by these NP forms, and on the whole data set.


The last two values, P (G|F ) and P (G), can be computed from unlabeled data.

given the precision of an arbitrary rule G, we can compute the precision of any otherview rule F .


However, because of the high volume of calls, spoken dialogue service providers typically can only afford to store, transcribe, and analyze a small fraction of the dialogues.

there is a great need for methods for both automatically evaluating system performance, and for extracting subsets of dialogues that provide good training data for system improvement.


The fact that the interaction went well until this point is not captured by the Task Completion metric.

we decided to use both User Satisfaction and Task Completion as response variables in separate experiments in order to allow the PDI to capture the relationship between these two variables.


On the other hand, the development test set was randomly selected by us from our pool of Arabic articles and then submitted to the human translator.

the human translations in the blind set are generally more accurate than the human translations in the development test.


For example, the adjective "unpredictable" may have a negative orientation in an automotive review, in a phrase such as "unpredictable steering", but it could have a positive orientation in a movie review, in a phrase such as "unpredictable plot".

the algorithm extracts two consecutive words, where one member of the pair is an adjective or an adverb and the second provides context.


First, lexicon entries that are marked as preferred by the shallow component are assigned higher priority than the rest.

the probability of finding the correct reading early should increase without excluding any reading.


Using Bayes rule, we flip this so we end up maximizing .

we are left with modelling two probability distributions: , the probability of a document ¥ given a summary ' , and , the probability of a summary.


This ranking algorithm performs relatively well, but it also results in many ties.

it is necessary to apply some filtering mechanism to maintain a reasonably sized sentence pool for final presentation.


Recall that MDL-DT is designed to produce as small a decision tree as possible.

the resulting tree would have a very small number of nodes covering the entire data space.


This problem could happen with C4.5, but in MDL-DT, which covers a large space with a few nodes, points in a region could be far apart, making the problem more acute.

the poor performance of MDL-DT may be attributable to its extensive use of pruning.


For one training example of a multi-class problem, this method creates a positive training example for the true class and negative training examples for the other classes.

positive and negative examples for each class are generated.


It is never used for particles, which are always written in hiragana.

it is more probable that a boundary exists between a kanji character and a hiragana character.


1 Since high precision is the most important criterion for the filter, we also examine the performance of the classifier as it is applied with a threshold.

a probability cutoff is set such that only positive classifications that exceed this cutoff are actually classified as legitimate.


1 Precision and Recall here refer only to the output of the extraction patterns.

100% recall indicates that all legitimate concept-instance pairs that were extracted using the patterns, were classified as legitimate by the filter.


The same was noticed for the cw feature from FS2.

we decided to add two new features, namely the parts of speech of the head word and the content word respectively.


Similarly, since is regular, the weighted transduction defined by is rational.

by the theorem of Schützenberger (Schützenberger, 1961), there exists a weighted transducer defined over the alphabet d and the probability semiring realizing that transduction.


Indeed, the corresponding automaton may have Òì transitions.

even if the vocabulary size is just 1,000, the representation of a classical trigram model may require in the worst case up to one billion transitions.


One consists of expanding that WFA ondemand.

in some speech recognition systems, the states and transitions of the language model automaton are constructed as needed based on the particular input speech utterances.


In this section, we present a new method for creating an exact representation of ¢ -gram language models with WFAs whose size is practical even for very largevocabulary tasks and for relatively high ¢ -gram orders.

our representation does not suffer from the disadvantages just pointed out for the two classical methods.


Furthermore, due the Viterbi approximation used in most speech processing applications, the weight associated to a string § by a weighted automaton representing the model is the minimum weight of a path labeled with § .

an ¢ -gram language model is represented by a WFA over the tropical semiring.


It is the default transition taken at state when does not admit an outgoing transition labeled with the word considered.

failure transitions have the semantics of otherwise.


Thus, a transition that could only be taken in the absence of any other alternative in the exact representation can now be taken regardless of whether there exists an alternative transition.

the approximate representation may contain paths whose weight does not correspond to the exact probability of the string labeling that path according to the model.


Let This can be viewed as a specific obligatory weighted context-dependent rewrite rule where the left and right contexts are not restricted (Kaplan and Kay, 1994;Mohri and Sproat, 1996).

the transduction corresponding to the class Í c an be viewed as the application of the following obligatory weighted rewrite rule: The direction of application of the rule, left-to-right or right-to-left, can be chosen depending on the task 2 .


Thus, the transduction corresponding to the class Í c an be viewed as the application of the following obligatory weighted rewrite rule: The direction of application of the rule, left-to-right or right-to-left, can be chosen depending on the task 2 .

these ¢ classes can be viewed as a set of batch rewrite rules (Kaplan and Kay, 1994) which can be compiled into weighted transducers.


We are also interested in the algorithm's performance in recognising non-literal readings.

we compute precision (P ), recall (R), and Fmeasure (F ), where A is the number of non-literal readings correctly identified as non-literal (true positives) and B the number of literal readings that are incorrectly identified as non-literal (false positives): The baseline used for comparison is the assignment of the most frequent reading literal.


In our experiments, the most successful one exploits the facts that relax II performs better than relax I on subjects and that relax I performs better on the other roles.

the algorithm combination uses relax II if the test instance is a subject, and relax I otherwise.


/e4 Part of a literal translation of a Japanese article: j1 At about 8:55 a.m. on the 29th, Kimiko Tanaka, 64, the wife of Bungei Shunju's president Kengo Tanaka, 65, found bullet holes on the eastern wall of their two-story house at 4 Akabane Nishi, Kitaku, Tokyo.

/j1 j2 of an investigation, the officers of the Akabane police station found two holes on the exterior wall of the bedroom and a bullet in the bedroom.


However, the relation between English news sentences and Japanese news sentences are not literal translations.

the results for sentence alignments include many incorrect alignments.


However, when we compare the validity of two sentence alignments in different article alignments, SntScore prefers the sentence alignment with the more similar (high AVSIM) article alignment even if their SIM has the same value, while SIM cannot discriminate between the validity of two sentence alignments if their SIM has the same value.

sntscore is more appropriate than sIM if we want to compare sentence alignments in different article alignments, because, in general, a sentence alignment in a reliable article alignment is more reliable than one in an unreliable article alignment.


The most important category for sentence alignment is one-to-one.

we want to discard as many errors in this category as possible.


However, they obtained a lower precision of 69.8% for the NHK corpus.

the precision of their method depends on the corpora.


By storing partially completed arcs in the chart and interleaving the inner two loops, complexity of O(|T |n 3 m!2 m ) can be achieved.

while the algorithm is exponential in m, the fan-out of the grammar, it is polynomial in the size of the input string.


The nonterminals on the target side are ignored entirely, and while the alignment algorithm considers possible pairs of nodes as elementary trees on the target side during training, the generative probability model should be thought of as only generating single nodes on the target side.

the alignment algorithm is constrained by the bracketing on the target side, but does not generate the entire target tree structure.


Computing the alignment between the 2m children of the elementary tree on either side requires choosing which subset of source nodes to delete, O(2 2m ), which subset of target nodes to insert (or clone), O(2 2m ), and how to reorder the remaining nodes from source to target tree, O((2m)!).

overall complexity of the algorithm is O(|T | 2 m 2 4 2m (2m)!


When the system attempts to link of and de, the new link will induce the dotted dependency, which crosses a previously induced dependency between service and données.


22 roles were copied to mark reference to conjunction.

all in all there was a stock of 54 grammatical roles.


In WordNet, one synset consists of several synonyms which represent a single sense.

polysemous words occur in more than one synsets.


For example, <depend on, OBJ, illumination> and <turn on, OBJ, light> is not a synonymous pair.

it is important to filter out these inappropriate candidates.


For example, in sentences "They turned on the lights" and "They depend on the illumination", the meaning of two collocations <turn on, OBJ, light> and <depend on, OBJ, illumination> are different although their contexts are the same.

monolingual information is not enough to estimate the similarity of two collocations.


The precisions are 0.3190, 0.4922, and 0.6811 for Method 1, Method 2, and our method, respectively.

the precisions of our approach are higher than the other two methods even when their recalls are the same.


If we use |A|, |B|, |C| to denote correct pairs extracted by Method 3, our method, both Method 3 and our method respectively, we get |A|=100, |B|=295, and .

the synonymous collocation pairs extracted by our method cover 78% ( ) of those extracted by Method 3 while those extracted by Method 3 only cover 26% ( ) of those extracted by our method.


Computing the similarity between a vector a and this subspace B is computationally more expensive than for the negation of Theorem 1, because the scalar product of a with (up to) n vectors in an orthogonal basis for B must be computed.

the gain we get by comparing each document with the query a NOT b using only one scalar product operation is absent for disjunction.


The experiments will show that the baseline ITG constraints are not sufficient on the Canadian Hansards task.

we present an extension to the ITG constraints.


If arbitrary word-reorderings are allowed, the search problem is NP-hard (Knight, 1999).

we have to restrict the possible reorderings in some way to make the search problem feasible.


Here, we initialize this table with monotone translations of IBM Model 4.

q 0 j l ,j r ,e b ,e t denotes the probability of the best monotone hypothesis of IBM Model 4.


The second pruning technique is threshold pruning: the idea is to remove all hypotheses that have a low probability relative to the best hypothesis.

we introduce a threshold pruning parameter q, with 0 ≤ q ≤ 1.


In the following sections we will present results on two tasks.

in this section we will show the corpus statistics for each of these tasks.


On this task, the translation quality of the search with the baseline ITG constraints is already competitive with the results for the IBM constraints.

we expect the search with the extended ITG constraints to outperform the search with the IBM constraints.


Note that many of the used feature functions are derived from probabilistic models: the feature function is defined as the negative logarithm of the corresponding probabilistic model.

the feature functions are much more 'informative' than for instance the binary feature functions used in standard maximum entropy models in natural language processing.


The confidence value reflects the probability that the candidate is coreferential to the NP in the overall distribution 2 , but not the conditional probability when the candidate is concurrent with other competitors.

the confidence values are unreliable to represent the true competition criterion for the candidates.


While the classification in the single-candidate model can find its interpretation in the twincandidate model, it is not true vice versa.

we can safely draw the conclusion that the twin-candidate model is more powerful than the single-candidate model in characterizing the relationships among an anaphor and its candidates.


These data noises may hamper the training of a goodperformanced classifier, and also damage the accuracy of the antecedent selection: too many comparisons are made between incorrect candidates.

in order to reduce the computational cost and data noises, an effective candidate filtering strategy must be applied in our approach.


Nevertheless, the precision drops largely due to the numerous invalid NPs in the candidate set.

a significantly low Fmeasure is obtained in their approach.


Figure 3 illustrates detailed discussion of the (Kaplan and Maxwell, 1996) operators is found in (Clément and Kinyon, 2003).

10 the grammars we generate exhibit redundancies for modifiers, but, since the MG hierarchy has relatively few redundancies, and since these grammars are automatically generated, the problem is minor.


The pattern matching system, given a set of extraction patterns, classifies a document as retrieved if any of the patterns match any portion of the document, and as random otherwise.

we can get the performance of text classification of the pattern matching system in the form of a precision-recall curve, without any supervision.


3 After receiving the speech recognition hypothesis "uh two p.m.," the system cannot tell whether the user utterance corresponds to a dialogue act specifying the start time or the end time (da1,da2).

the system tries to obtain further information about the time.


The simple bigram of dialogue states and dialogue acts is not sufficient due to the complexity of the data that a dialogue state possesses, which can cause data sparseness problems.

we classify the ways that dialogue states are changed by dialogue acts into 64 classes characterized by seven binary attributes (Table 2) and compute the occurrence probability of each class in the corpora.


This is reasonable as most queries posed by the users on the Web tend to be short with only a few characters (Pu 2000).

the accurate extraction of CSS and its separation into basic components is essential to alleviate the speech recognition errors.


Most basic CSS components should be mapped to one of these phrases.

even if a basic component contains errors, as long as we can find a sufficiently similar phrase in the phrase dictionary, we can use this in place of the erroneous CSS component, thus eliminating the errors.


The three level of matching also ranges from being more exact at the character level, to less exact at the confusion syllable level.

if we can find a relevant phrase with sim(q i ,c j )>¡ at the higher character level, we will not perform further matching at the lower levels.


However, the speech interface involves two inevitable problems: one is speech recognition errors, and the other is that much information cannot be conveyed at once in speech communications.

the dialogue strategies, which determine when to make guidance and what the system should tell to the user, are the essential factors.


On the other hand, for inhabitants, it is useful to omit too obvious information and to output additive information.

we introduce a dimension that represents the knowledge level on the target domain.


Especially in the bus system, the conciseness is preferred because the bus information is urgent to most users.

we also take account of degree of hastiness of the user, and accordingly change the system's responses.


Such additive information is usually cooperative, but some people may feel such a response redundant.

we introduce the user models and control the generation of additive information.


For instance, discourse marker insertion algorithms assume that sentence plans are static objects.

any change to the static nature of sentence plans will inevitably disrupt them.


Fortunately, there are only a few hundred Chinese characters that are particularly common in transliterations.

an FN candidate would be generated given S', if it contains only characters stored in a transliterated name character list (which contains 618 Chinese characters).


The enumeration of all possible chunkings J and E together with word alignment A and chunk alignment A requires a significant amount of computation.

we have introduced a variation of the Inside-Outside algorithm as seen in (Yamada and Knight, 2001) for Estep computation.


Specifically, it assumes that the classes of the classifiers in bootstrapping do not overlap.

bilingual bootstrapping is different from co-training.


Instead of passively using all the given labelled instances for training as in supervised learning, active learning repeatedly asks a supervisor to label what it considers as the most critical instances and performs training with the labelled instances.

active learning can eventually create a reliable classifier with fewer labelled instances than supervised learning.


But there are hundreds of sub-types of ORG that cannot be covered by less than a dozen concept-based seeds, which we used.

the recall of ORG is significantly affected.


In the absence of a good stopping criterion, the resulting list of patterns must be manually reviewed by a human; otherwise one can set ad-hoc thresholds, e.g., on the number of allowed iterations, as in (Riloff and Jones, 1999), or else to resort to supervised training to determine such thresholds-which is unsatisfactory when our goal from the outset is to try to limit supervision.

the lack of natural stopping criteria renders these algorithms less unsupervised than one would hope.


When a learner runs in isolation, it is in a sense undergoing "mono-training": the only evidence it has on a given iteration is derived from its own guesses on previous iterations.

once it starts to go astray, it is difficult to set it back on course.


In counter-training they proceed by finding their own weakly reliable positive evidence, and by providing each other with reliable negative evidence.

in effect, the unsupervised learners "supervise" each other.


We have to consider  For the converse, (for technical reasons and to ease proofs) we use both free group and free pregroup models of NL since a sequent is valid in NL only if its interpretation is valid in both models.

the free group translation for the types of G n is: ] we get : the free pregroup translation for the types of G n is: we get: Proof: Let τ n denote the type assignment by the rigid grammar G n .


-By induction hypothesis: (ii) nbalt(X qq l ) ≤ nbalt(qq r Y ) (similar for p).

-from (i) (ii): nbalt(Xqq l ) ≤ nbalt(qq r Y ) (similar for p).


Consider the following sentence: This derivation expresses the following dependencies: We assume again that heads are generated before their modifiers or arguments, and that word-word dependencies are expressed by conditioning modifiers or arguments on heads.

the head words of arguments (such as Smith) are generated in the following manner: The head word of modifiers (such as yesterday) are generated differently: Like Collins (1999) and Charniak (2000), the SD model assumes that word-word dependencies can be defined at the maximal projection of a constituent.


Therefore, they are only conditioned on their lexical categories.

this model contains the following three kinds of word probabilities: 1.


As explained in section 5.2, there are instances where the categorial functor is conditioned on its arguments -the example given above showed that verbs in relative clauses are conditioned on the lexical head of the noun which is modified by the relative clause.

the inside probability of a constituent cannot include the probability of any lexical head whose argument slots are not all filled.


Furthermore, all minimal solved forms are trees since we assumed connectedness (Prop.2).

all minimal solved forms are simple, so they have configurations (Lemma 1).


Although their algorithm captures prefix-suffix combinations or circumfixes, it does not handle the multiple affixes per word we observe in Arabic.

we choose a segmentation into multiple prefixes and suffixes.


crosslanguage ambiguity) for which theoretical solutions are needed; iv) have been a focus of linguistic and/or psycholinguistic research; v) have a syntactic analysis that is broadly agreed on.

the following have been included: • By criteria (i) and (ii): negation, nondeclarative sentences (questions, imperatives).


A key feature of the model is that parameter values are not simply the standardly presumed 0 or 1, but rather bits of tree structure or treelets.

a grammar, in the STL sense, is a collection of treelets rather than a collection of 1's and 0's.


In particular, while v = 3, h ≤ 2 markovization is good on its own, it has a large number of states and does not tolerate further splitting well.

we base all further exploration on the v ≤ 2, h ≤ 2 grammar.


The most interesting result, though, is the difference in speed and in antecedent recovery accuracy between the parser that inserts traces, and the parser which uses perfect information from the treebank about the sites of EEs.

the question naturally arises: could EEs be detected before parsing?


Our method cleans the rules in advance by only focusing on the remaining errors.

our method complements on-line processing and adapts translation rules to the given conditions of the MT engine.


BLEU cancels out individual errors by summing the similarities of MT results.

we need all of the MT results from the evaluation corpus in order to calculate an accurate BLEU score.


This method is carried out by selecting or removing translation rules to increase the BLEU score of the evaluation corpus ( Figure 1).

this task is regarded as a combinatorial optimization problem of translation rules.


Leave one piece for the evaluation, acquire rules from the rest (N − 1) of the pieces, and repeat them N times.

we obtain N pairs of rule set and evaluation sub-corpus.


The characteristics common to these methods, including BLEU, is that the similarity to references are measured for each sentence, and the evaluation score of an MT system is calculated by aggregating the similarities.

mT results of the evaluation corpus are necessary to evaluate the system, and reducing the number of sentence translations is an important technique for all of these methods.


Even within the same language, the same referent can be encoded countably or uncountably: clothes/clothing, things/stuff , jobs/work.

we must learn countability classes from usage examples in corpora.


demand is both countable and uncountable.

rather than classifying a given target noun according to the unique most plausible countability class, we attempt to capture its full range of countabilities.


Most of the previous Korean noun extraction systems use a morphological analyzer or a Partof-Speech (POS) tagger.

they require much of the linguistic knowledge such as morpheme dictionaries and rules (e.g.


We have also observed that a noun belongs to a morpheme as well as a word.

we do not have to do morphological analysis in the noun extraction point of view.


This means that our method does not require any dictionary or linguistic knowledge.

without manual labor to construct and maintain those resources, our method can extract nouns by using only the statistics, which can be automatically extracted from a POS tagged corpus.


As it took over two years to tag one tenth of the CSJ accurately, tagging the remainder with morphological information would take about twenty years.

the remaining nine tenths of the CSJ must be tagged automatically or semi-automatically.


As we mentioned in Section 1, tagging the whole of the CSJ manually would be difficult.

we are taking a semi-automatic approach.


Their method uses a model that estimates how likely a string is to be a morpheme as its probability, and thus it has a potential to overcome the unknown word problem.

we use their method for morphological analysis of the CSJ.


We believe that the reason for this is that definitions of a word segment and its POS category depend on a particular corpus, and the definitions from corpus to corpus differ word by word.

we need to put only words extracted from the same corpus into a dictionary.


To estimate actual pronunciation by using only the basic form and a dictionary is impossible.

actual pronunciation is assigned to results of morphological analysis by aligning the basic form and pronunciation in the CSJ.


This result indicates that our morpheme models could detect both known and unknown words accurately, especially long words.

we investigated the recall of unknown words in the test corpus, and found that 55.7% (928/1,667) of short word segments and 74.1% (2,660/3,590) of long word segments were detected correctly.


Instead of positional constraints, they have overt postpositions that restrict the syntactic relation and composition of phrases.

unless we concentrate on the postpositions, we must enlarge the neighboring window to get a good hypothesis.


Since chunking is usually processed in the earlier step of natural language processing, the errors made in this step have a fatal influence on the following steps.

the exceptions that are ignored by the rules must be com-  pensated for by some special treatments of them for higher performance.


There are four basic phrases in Korean: noun phrase (NP), verb phrase (VP), adverb phrase (ADVP), and independent phrase (IP).

chunking by rules is divided into largely four components.


Because the important unit of the work in the applications of text chunking is a phrase, F-score is far more important than accuracy.

we have much room to improve in F-score.


The more important is an attribute, the larger is the weight of it.

the most important attribute among 17 attributes is C i−1 , the chunk type of the previous word.


Specifically the supertag of a word is determined by the appearances of certain words, POS tags, or supertags in its context.

sNoW is suitable for the supertagging task.


in the SENSEVAL initiative (Kilgarriff, 2001), a notable exception being the Prague Treebank (Hajičová, 1998) .

most recent work in corpus-based semantics has taken an unsupervised approach, relying on statistical methods to extract semantic regularities from raw corpora, often using information from ontologies like WordNet (Miller et al., 1990).


We tag all FEEs that occur in the corpus with their appropriate frames, and specify their frame elements.

our focus is different from the lexicographic orientation of the FrameNet project mentioned above.


However, exhaustive word sense annotation is a highly timeconsuming task.

we decided for a selective annotation policy, annotating only the heads of frame elements.


But, to uncover the function of these nonverbal signals, we must examine how listener's nonverbal behavior affects the speaker's following action.

we looked at two consecutive Assertion UUs by a direction-giver, and analyzed the relationship between the NV status of the first UU and the direction-giving strategy in the second UU.


MACK then utters the second segment as before, but this time the GrM, finds that the user was looking up at MACK during most of the UU as well as after it, which signals that the UU is not grounded.

the RP generates an elaboration (line 4).


As we can see in Figure 1, there are many local minima that do not correspond to actual boundaries.

we ignore all potential boundaries with a probability lower than p limit .


There is strong evidence that competition and ranking of these constraints is important to achieve an optimal interpretation.

we have developed an optimization approach for multimodal interpretation, particularly for interpreting multimodal references.


The focus of attention from the prior conversation shapes how users refer to those objects, and thus, influences the interpretation of referring expressions.

we need to simultaneously consider the temporal relations between the referring expressions and the gestures, the semantic constraints specified by the referring expressions, and the contextual constraints from the prior conversation.


For example, in the referring expression the Victorian house, the style feature is Therefore, an object can only be a possible referent if the style of that object is Victorian.

we define the following: Note that these feature constraints are dependent on the specific domain model for a particular application.


As discussed in Section 2, a hard constraint concerning temporal relations between referring expressions and gestures will be incapable of handling the flexibility of user temporal alignment behavior.

the temporal constraint in our approach is a graded constraint, which is defined as follows: This constraint indicates that the closer a referring expression and a potential referent in terms of their temporal alignment (regardless of the absolute precedence relationship), the more compatible they are.


The first critical step is to guess as well as possible the meaning of the unknown word(s).

suppose the sentence is "fall ball", resulting in the semantic structure: [8] unit1 ?ev fall(?ev,?state), fall-1(?ev,?obj) If this structure is matched, bindings for ?ev and ?obj are found.


Note that the language model generates the source string X.

the weights of the TAG rules condition on the words in X, but do not generate them.


The first feature we define is Φ 0 (x, y) as the log-probability of y given x under the lattice produced by the baseline recognizer.

this feature will include contributions from the acoustic model and the original language model.


The context costs between f i and all the units in the preselection list of the phonetic context ρ i are computed and the M units with the best context cost are selected: The feature costs between f i and the units in U i are then computed and the N units with the best target cost are selected: The unit sequence u verifying: is determined using a classical Viterbi search.

for each position i, the N 2 concatenation costs between the units in U i and U i+1 need to be computed.


As mentioned before, the transducerĜ represents an n-gram backoff model for the joint probability distribution P (x, u).

backoff transitions are used in a standard fashion whenĜ is viewed as an automaton over paired sequences (x, u).


Let w be a unit and let h be its conditioning history within the n-gram model.

for a given shrink factor γ, the transition corresponding to the n-gram hw is removed from the weighted automaton if: where h is the backoff sequence associated with h. a higher-order n-gram hw is pruned when it does not provide a probability estimate significantly different from the corresponding lower-order n-gram sequence h w. This standard shrinking method needs to be modified to be used in the case of our halfphone-to-unit weighted transducer model with the restriction on the traversal of the backoff transitions described in the previous section.


The shrinking methods must take into account all the transitions sharing the same input label at the state identified with h and its backoff state h .

at each state identified with h in G, a transition with input label x is pruned when the following condition holds: where h is the backoff sequence associate with h and X x k is the set of output labels of all the outgoing transitions with input label x of the state identified with k. We used the AT&T Natural Voices Product speech synthesis system to synthesize 107,987 AP news articles, generating a large corpus of 8,731,662 unit sequences representing a total of 415,227,388 units.


It assumes that the speech recognizer will make errors when the caller attempts to identify himself.

perfect matches with the database entries will be rare.


Therefore perfect matches with the database entries will be rare.

for each record in the database, we attach a measure of the probability that the record is the target record.


When additional identifying information arrives, the system updates these probabilities using Bayes' rule.

the system might begin with a uniform probability estimate across all database records.


In contrast, the parameters of the generative model only include words which are either already incorporated into the structure, or are the immediate next word to be incorporated.

it is relatively easy to determine the significance of each word.


Proximity in the string is our only indication of relevance.

we compute l(yield(d i ,..., d m )) by running a recurrent neural network backward over the string, so that the most recent input is the first word in the lookahead string, as discussed in more detail in (Henderson, 2003a).


Note that the most complex step of the method is finding z i = arg max z∈GEN(xi) Φ(x i , z)•ᾱ -and this is precisely the decoding problem.

the training algorithm is in principle a simple part of the parser: any system will need a decoding method, and once the decoding algorithm is implemented the training algorithm is relatively straightforward.


That is, the over-fitting problem arises if large "substructures" are used in the kernel calculations.

the machine learning approach can never be trained efficiently.


The number of substructures in the input object usually becomes exponential against input object size.

all kernel valuesK(X, Y ) are nearly 0 except the kernel value of the object itself,K(X, X), which is 1.


We note, however, that kernels are calculated using the DP algorithm.

it is not clear how to calculate kernels efficiently with a statistical feature selection method.


The recursive calculation of Equations (12) to (14) and Equations (16) to (19) can be executed in the same way and at the same time in parallel.

statistical feature selection can be embedded in oroginal sequence kernel calculation based on a dynamic programming technique.


In the classification part, the features (subsequences) selected in the learning part must be known.

we store the TRIE of selected sub-sequences and use them during classification.


This is because we can convert to the original sub-tree form from the string encoding representation.

we can calculate tree kernels with statistical feature selection by using the original tree kernel calculation with the sequential pattern mining technique introduced in this paper.


Consequently, the classifier trained on the "perfect" feature vectors would probably fail to output anticipated results on the noisy data during real resolution.

we modify the training and testing procedures of the system.


The scores of recall are even lower than those of Baseline.

in comparison with Optimal, we see the degrade of the F-measure and the success rate, which confirms our hypothesis that the classifier learned on perfect training instances would probably not perform well on the noisy testing instances.


The algorithm converges fast probably because in the data set, most of the antecedent candidates are non-pronouns (89.1% for MUC-6 and 83.7% for MUC-7).

the ratio of the training instances with backward features changed may be not substantial enough to affect the classifier generation.


As shown in Section 2, the search space of the coreference problem can be represented by the Bell tree.

the search problem reduces to creating the Bell tree while keeping track of path scores and picking the top-N best paths.


In the ACE annotation, every mention has an entity type.

we can eliminate hypotheses with two mentions of different types.


In recent years there has been a lot of work to develop anaphora resolution algorithms using both symbolic and statistical methods that could be quantitatively evaluated (Humphreys et al., 1997;Ng and Cardie, 2002) but this work focused on identity relations; bridging references were explicitly excluded from the MUC coreference task because of the problems with reliability discussed earlier.

most work on bridging has been theoretical, like the work by Asher and Lascarides (1998).


In particular, to our knowledge, there have been no attempts to (1) globally optimize an anaphoricity determination procedure for coreference performance and (2) incorporate anaphoricity into coreference systems as a feature.

as part of our investigation, we propose a new corpus-based method for achieving global optimization and experiment with representing anaphoricity as a feature in the coreference system.


The challenge then is to determine a "good" degree of conservativeness.

we can design a parametric anaphoricity model whose conservativeness can be adjusted via a conservativeness parameter.


Because some alphabet writing systems use various diacritical marks, we find it more practical to write names containing such diacriticals as they are rendered in English.

we refer all foreign-Chinese transliteration to English-Chinese transliteration, or E2C.


After experiments, we found that each of the 13-fold open tests gave consistent error rates with less than 1% deviation.

for simplicity, we randomly select one of the 13 subsets, which consists of 2896 entries, as the standard open test set to report results.


The reason for this is that word-aligned parallel corpora do not generally exist.

word level alignments are treated as hidden variables.


Here s and w index the N s sentence-aligned sentences and N w word-aligned sentences in our corpora respectively.

m combines the expected complete log likelihood and the complete log likelihood.


Second, person names and specialized terminology are among the most frequent mixed-lingual items.

we would expect that the appearance of both representations in close proximity on the same pages gives us more confidence in the kanji representations.


the Kun reading of ¡ is "ta", while its On reading is "den".

using the mappings from the Unihan database, we failed to obtain the mapping between the pronunciation "da" and the kanji ¡ , which resulted in both low precision and recall for "matsuda".


"The protein R activates X" fails this requirement, as R might instead activate X post-translationally.

whether the event should be extracted or not depends on the type of the accusative object X (e.g.


This is in fact yet a different task than those mentioned above, say authorship sorting.

we will report on the results with renormalization, but only as additional information.


The large and complex structure of TFS descriptions (Carpenter, 1992) leads to slow unification times, affecting the parsing times.

failing unifications must be avoided during retrieval from the chart.


As the WERs are similar, we conclude that parsing n-best lists requires more work than parsing n-best lattices, for the same result.


As is normally the case in CLP, deductive search is used alongside constraint resolution.

under such assumptions, our principles can be converted to: with an implementation of type-antecedent constraints and an implementation of whenfs/2 (Section 3.3), which delays the goal in its second argument until v is subsumed by (one of) the most general satisfier(s) of description α, all that remains is a method for finding the trigger, the most efficient type antecedent to use, i.e., the most general one that will not violate soundness.


If this rule is applied exhaustively to an MRS M, we obtain an MRS M without EPconjunctions.

it should be intuitively clear that the configurations of M and M correspond; the configurations of M also correspond to the minimal solved forms of the translation of M .


While labeled data are difficult to obtain, unlabeled data are readily available and plentiful.

this paper advocates using a bootstrapping framework and a feature projection technique with just unlabeled data for text categorization.


When the machine-labeled data is used to learn a text classifier, there is another difficult in that they have more incorrectly labeled documents than manually labeled data.

we develop and employ the TCFP classifiers with robustness from noisy data.


The window is a slide from the first word of the document to the last in the size of the window (60 words) and the interval of each window (30 words).

the final output of preprocessing is a set of context vectors that are represented as content words of each context.


As the first task, we built up a new training data set; it consists of 500 different documents randomly chosen from appropriate categories like the experiment in (Slonim et al., 2002).

we report performances from two kinds of Naive Bayes classifiers which are learned from 500 training documents and the whole training documents respectively.


As shown in Table 4, especially we could achieve significant improvement in the WebKb data set.

we find that the new method for choosing keywords is more useful in a domain with confused keywords between categories such as the WebKB data set.


Since our method does not use any labeled data, it cannot be fairly compared with the former approaches.

we compare our method with a clustering technique.


Labeled data are expensive while unlabeled data are inexpensive and plentiful.

our method is useful for low-cost text categorization.


This makes it possible to use a large underlying corpusin this case, the entire Internet as seen through the AltaVista search engine.

estimates for model parameters are subject to a relatively small amount of random variation.


Words that co-occur more frequently with one anchor than the other are themselves taken to be predictive of sentiment.

information about a pair of words is generalized to many words, and then to documents.


For example, poor can refer to an objective economic state that does not necessarily express negative sentiment.

a word such as income appears 4.18 times as frequently with poor as excellent, even though it does not convey negative sentiment.


For such problems, it is often difficult or expensive to obtain labeled data.

a change in modeling strategies is needed, towards approaches that require less supervision.


So it is not unreasonable to suppose that the intended sense for prevention when translated as prevención is different from that of bar.

the intended sense is most likely to be control.


The relevant English senses for any Spanish sense is given by its ¥ P .

the union of the s of all the Spanish senses in the cluster forms the set of English senses for each concept.


Aided with a concept variable, it is possible to model the same relationship by creating a separate Spanish sense that contains these two words and relating this Spanish sense with the ten English senses through a concept variable.

these words now need to belong to only one sense as opposed to ten.


These variables are assumed to be independent given a hidden variable c, the sense of the target verb.

the joint probability of the observed variables (features) for each verb instance, i.e., each parsed sentence containing the target verb, is defined in equation 1, The i f 's are discrete-valued features that can take multiple values.


Unfortunately, unbalanced sense distribution is the usual case in WSD tasks, which makes NMI itself hard to interpret.

in addition to NMI, we also give its upper bound (upper-NMI) and the ratio of NMI and its upper bound (NMI-ratio) for each verb, as shown in columns 6 to 8 in Table 2.


This algorithm learns parameters from explicit associations between the class and the features, or combination of features, that characterize it.

such systems are very sensitive to the training data, and those data are, generally, assumed to be as clean as possible.


However, this correlation is a low 9 4 A C B E 8 8 .

we observe a low correlation between STE and PR, ( .


On the other hand, holiday has a relatively high STE value of .

we conclude that STE alone is not a good direct indicator of PR.


One effect of the relabelling procedure described above is the recovery of Penn functional tags.

it is informative to compare our results with those reported in (Blaheta and Charniak, 2000) for this same task.


there is no overlapping between the words of two arguments.

two semantic structures F p 1 ,a 1 and F p 2 ,a 2 1 , associated with two different ar- guments, cannot be included one in the other.


is not considered part of the semantic structure.

in step 1, an argument structure F p,a is mapped in a fragment set F p,a .


It follows that φ i (F x ) = n∈Nx I i (n), where N x is the set of the F x 's nodes.

the kernel can be written as: where N x and N z are the nodes in F x and F z , respectively.


On the other hand, users might get frustrated with a system that behaves too cautiously and rejects or ignores too many utterances.

an important feature in dialogue system engineering is the tradeoff between avoiding task failure (due to misrecognitions) and promoting overall dialogue efficiency, flow, and naturalness.


We split the out-ofgrammar class into two sub-classes depending on whether the 10-best recognition results include at least one hypothesis with a WER ≤ 50 compared to the corresponding transcription.

if there is a recognition hypothesis which is close to the transcription, an utterance is labeled as oog (WER ≤ 50).


Sentences at the beginning of a paragraph are usually more important than sentences that are further down in a paragraph, due in part to the way people are instructed to write.

probably the simplest approach conceivable to sentence ranking is to choose the first sentences of each 1 We did not use any machine learning techniques to boost performance of the algorithms we tested.


Therefore, probably the simplest approach conceivable to sentence ranking is to choose the first sentences of each 1 We did not use any machine learning techniques to boost performance of the algorithms we tested.

performance of the algorithms tested here will almost certainly be below the level of performance that could be reached if we had augmented the algorithms with such techniques (e.g.


The paragraphbased rankings do not provide scaled importance rankings but only "important" vs. "not important".

in order to compare human rankings to the paragraph-based baseline approach, we calculated point biserial correlations (cf.


The intention is that x e has value 1 if e is used in the tour, and 0 otherwise.

the cost of the tour can be written as e∈E c e x e .


Here, φ i (x) = 1 if word i occurs in document x.

the kernel function K(x, y) returns the num-ber of words in common between x and y.


However, if φ m (t i ) = φ m (t j ), then we proceed to compute the similarity s(t i , t j ).

restricting nodes by m(t i , t j ) is a way to prune the search space of matching subtrees, as shown below.


Note that, although training was done over all 24 relation subtypes, we evaluate only over the 5 high-level relation types.

classifying a RESIDENCE relation as a LOCATED relation is deemed correct 4 .


We run the parser on all sentences, and we note for which sentences the parser is successful.

we define the parsability of a word R(w) as the ratio of the number of times the word occurs in a sentence with a successful parse (C(w|OK)) and the total number of sentences that this word occurs in (C(w)): if a word only occurs in sentences that cannot be parsed successfully, the parsability of that word is 0.


Yet, if w is very frequent, R(w) is more reliable than if w is not frequent.

as an alternative, we also experimented with a set-up in which an exact binomial test is applied to compute a confidence interval for R(w).


An obvious issue is that one does not expect the phrase structure representation of English (as produced by state-of-the-art treebank parsers) to carry over to less configurational languages.

(Hwa et al., 2002) extract a more language-independent dependency structure from the English parse as the basis for projection to Chinese.


With hindsight, however, the main issue in their experiments appears to be not their model, but that they randomly initialized the production (attachment) probabilities.

their learned grammars were of very poor quality and had high variance.


Even with a grammar in which the productions are initially uniform, a symbol X can only possibly have non-zero posterior likelihood over spans which contain a matching terminal X.

one can start with uniform rewrites and let the interaction between the data and the model structure break the initial symmetry.


Maximizing it is simple: set all x to be equiprobable (the uniform distribution).

a sufficiently small β drives up the importance of H relative to the other term, and the entire problem becomes concave with a single global maximum to which we expect to converge.


In the case of model 2 with the random-split initializer, SDA significantly outperformed EM (comparing both matches and crossing brackets per sentence under a binomial sign test, p < 10 −6 ); we see a > 5% reduction in average crossing brackets per sentence.

our strategy of using DA but modifying it to accept an initializer worked as desired in this case, yielding our best overall performance.


We take it here that the business of MEMT is about choosing among translation outputs from multiple MT systems, whether black box or not, for each input text.

the question we want to address is, how do we go about choosing among MT outputs so that we end up with a best one?


For a sentence-aligned corpus, the association measure between source and target words from two different sentence pairs should be exactly 0 because words should not be aligned across sentences.

the larger translation matrix (calculated on the entire corpus) is block diagonal, with non-zero association measures only in blocks corresponding to aligned sentence.


However, it is more convenient to use integers as state indices for an implementation.

algorithms usually maintain a mapping from abstract states to integer state indices.


Note that R<T> refers to a smart pointer of T. computed by traversing it.

central algorithms of the RWTH FSA toolkit are the depthfirst search (DFS) and the computation of strongly connected components (SCC).


It is very costly to store arbitrary types as arc labels within the arcs itself.

the RWTH FSA toolkit offers alphabets that define mappings between strings and label indices.


As arc labels are integers in the implementation and their meaning is bound to an appropriate symbolic alphabet, there is the need for symbolic mapping between different alphabets.

the toolkit provides the lazy map-input and map-output transducers, which map the input and output arc indices of an automaton to be compatible with the indices of another given alphabet.


If AER does not reflect the optimal balance between precision and recall for a particular application, then optimizing AER may not produce the best task-based performance for that application.

the next step in this research must be to test whether the improvements in AER we have demonstrated for Model 1 lead to improvements on task-based performance measures.


It can be easily seen that we must assign probability 1 to all transitions except τ c and τ d , since this is the only pair of distinct transitions that can be applied for one and the same top-of-stack symbol, viz.

whereas in the LR PPDA model we have we conclude that there is no proper assignment of probabilities to the transitions of the LR automaton that would result in a distribution on the generated language that is equivalent to the one induced by the source PCFG.


whereas in the LR PPDA model we have Thus we conclude that there is no proper assignment of probabilities to the transitions of the LR automaton that would result in a distribution on the generated language that is equivalent to the one induced by the source PCFG.

the LR strategy does not allow probabilistic extension.


However, such probability assignments are outside the reach of the usual training algorithms for PDAs, which always produce proper PDAs.

we may discard such assignments in the current study, which investigates aspects of the potential of training algorithms for CFGs and PDAs.


Furthermore, for each complete computation c there is a complete derivation d such that f (c ) = d, and p A (c ) = p G (d) > 0.

p A (τ ) > 0 for each transition τ , and p A (c) > 0, where c is the above-mentioned dead computation.


These quantities can be computed to any degree of precision, as discussed for instance in (Booth and Thompson, 1973) and (Stolcke, 1995).

normalization of a PCFG can be effectively computed.


We now map (A, p A ) to a language-equivalent PCFG (G , p G ), G = (Σ  , Q, X in , R ), where R contains the following rules with the specified associated probabilities: , for each X → XY ∈ ∆ with Z the unique stack symbol such that there is at least one transition XY → Z with Y ; Y ; • Y → ε with p G (X → ε) = 1, for each stack symbol Y such that there is at least one transition XY → Z ∈ ∆ or such that Y = X f in .

it is not difficult to see that there exists a bijection f from complete computations of A to complete derivations of G , and that we have for each complete computation c. (G , p G ) is consistent.


By combining equations (1) to (4) we conclude that, for each complete computation c of A, p A (c) = p G (f (c)) = p G (f (c)) = p A (c) = p G (f (c)).

our parsing strategy S can be probabilistically extended.


That is, he would obtain the final answer by discarding all answers to the simple questions which do not accomplish the restrictions imposed by the temporal signal provided by the original question (during).

the treatment of complex question is based on the decomposition of these questions into simpler ones, to be resolved using conventional Question Answering systems.


For the aforementioned phrase, this would consist of obtaining the names of all the men having been on the moon, ordering the dates and picking the second in the ordered list of names.

for this evaluation, we have just been focusing on trying to resolve the 112 left.


Clearly, if the base QA system is too poor, then the answers to the auxiliary questions will be useless; if the base system is highly accurate, the increase in accuracy will be negligible.

our approach seems most beneficial to middle-performance levels, which, by inspection of TREC results for the last 5 years, is where the leading systems currently lie.


Figure Skating Association, a writer for New Scientist, an Australian medical advisor to the WHO, and the general sales manager of Houseman, a supplier of water treatment systems.

the problem of word sense disambiguation has returned in a particularly nasty form.


To overcome this predicament, we aim to obtain more adjusted probability distributions of event pairs over their possible classes.

a probabilistic decision tree approach is preferred over conventional decision tree approaches (e.g.


Apply Bayesian rule to (5), we have:  PDT and NB are both supervised learning approach.

the training processes require many labeled cases.


However, this approach is not applicable to our work since only a few temporal indicators occur in each case.

we develop an alternative CB algorithm, i.e.


In our active learning process, we select a word sequence which consists of a machine-annotated named entity and its context rather than a single word.

all of the measures we propose for active learning should be applied to the m achineannotated named entities and we have to further study how to extend the measures for words to named entities.


Particularly, the calculation in SVM need be projected to a higher dimensional space by using a certain kernel function (,) ij K ww .

we adapt the cosine-similarity measure to SVM as follows: and may be regarded as a general vector-based similarity measure.


5): 2 Based on our data analysis, there is no observable difference in linguistic expressions involving frequently mentioned vs. occasionally occurring person names.

the use of frequently mentioned names in the corpus construction process does not affect the effectiveness of the learned model to be applicable to all the person names in general.


mentioned in the news almost always refer to unique entities.

we can retrieve contexts of these unambiguous names, and mix them together.


Since unigram matches do not distinguish words in consecutive positions from words in the wrong order, measures based on position-independent unigram matches are not sensitive to word order and sentence level structure.

systems optimized for these unigram-based measures might generate adequate but not fluent target language.


Using Equation 15 with β = 1 and S1 as the reference, S2's ROUGE-S score is 0.5, S3 is 0.167, and S4 is 0.333.

s2 is better than s3 and s4, and s4 is better than s3.


It is always possible to construct a mapping into a space where k acts as a dot product so long as k is a continuous kernel of a positive integral operator (Schölkopf et al., 1998).

we train the KPCA model using the following algorithm: 2.


As suggested in the introduction, the reason a rule-based approach works so well in this domain may be that empty categories are not naturally in the text, but are only inserted by the annotator, who is consciously following explicit linguistic principles, in this case, the principles of early GB theory.

the recovery of empty categories is, for the most part, more amenable to a rule-based approach than to a learning approach.